The central idea is that reasoning needs structure. To infer that one thing follows from another, a system must hold knowledge in a form where relationships are explicit — ontologies, knowledge graphs, and logical rules are common approaches.
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In the enterprise, knowledge representation is what turns scattered documents into something a system can act on. Once knowledge is represented formally, AI can trace a recommendation through defined steps instead of producing text that merely sounds right.